Analyzing in-plane distortion

ABSTRACT

Methods, systems, and non-transitory computer readable medium are described for generating assessment maps for corrective action. A method includes receiving a first vector map including a first set of vectors each indicating a distortion of a particular location of a plurality of locations on a substrate. The method further includes generating a second vector map including a second set of vectors by rotating a position of each vector in the first set of vectors. The method further includes generating a third vector map including a third set of vectors based on vectors in the second set of vectors and corresponding vectors in the first set of vectors. The method further includes generating a fourth vector map by subtracting each vector of the third set of vectors from a corresponding vector in the first set of vectors. The fourth vector map indicates a planar component of the first vector map.

TECHNICAL FIELD

The present disclosure relates to data integration, and, moreparticularly, analyzing in-plane distortion.

BACKGROUND

Products may be produced by performing one or more manufacturingprocesses using manufacturing equipment. For example, semiconductormanufacturing equipment may be used to produce wafers via semiconductormanufacturing processes. Sensors may be used to determine manufacturingparameters of the manufacturing equipment during the manufacturingprocesses. Metrology equipment may be used to determine property data ofthe products that were produced by the manufacturing equipment.

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In an aspect of the disclosure, a method may include receiving a firstvector map including a first set of vectors, each indicating adistortion of a particular location of a plurality of locations on asubstrate. The method further includes generating a second vector mapincluding a second set of vectors by rotating a position of each vectorin the first set of vectors. The method further includes generating athird vector map including a third set of vectors based on vectors inthe second set of vectors and corresponding vectors in the first set ofvectors. The method further includes generating a fourth vector map bysubtracting each vector of the third set of vectors from a correspondingvector in the first set of vectors. The fourth vector map indicates aplanar component of the first vector map.

In another aspect of the disclosure, a method may include receiving afirst vector map including a first set of vectors each indicating adistortion of a particular location of a plurality of locations on asubstrate. The method further includes generating a second vector mapincluding a second set of vectors by rotating a position of each vectorin the first set of vectors. The method further includes generating athird vector map including a third set of vectors based on vectors inthe second set of vectors and corresponding vectors in the first set ofvector components. The method further includes generating a fourthvector map including a fourth set of vectors by projecting a directioncomponent of each vector in the third set of vectors to a radialdirection. The method may further include generating a fifth vector maphaving a fifth set of vectors by grouping the vectors of the fourth setof vectors and determining a magnitude associated with each group ofvectors. The fifth vector map be a radial map and may indicate at leastone of stress or strain exhibited by the substrate.

In another aspect of the disclosure, a method may generate a sixthvector map including a sixth set of vectors by subtracting each vectorin the fifth set of vectors from a corresponding vector in the first setof vectors. The sixth vector map may be a residue map and may indicateabnormalities in the substrate.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an exemplary system architecture,according to certain embodiments.

FIG. 2 is a block diagram illustrating generation of assessment maps,according to certain embodiments.

FIG. 3 is a flow diagram of a method of generating planar maps,according to certain embodiments.

FIG. 4 is a flow diagram of a method of generating radial maps andresidue maps, according to certain embodiments.

FIGS. 5A-5E are graphs illustrating IPD maps and assessment maps,according to certain embodiments.

FIG. 6 is a block diagram illustrating a computer system, according tocertain embodiments.

DETAILED DESCRIPTION

Described herein are technologies directed to analyzing in-planedistortion. Vertical NAND (V-NAND) or 3D NAND memory typically stacksmemory cells vertically, and uses a charge trap flash architecture. Toconstruct, alternating layers of conducting and insulating film arestacked on a wafer substrate. By stacking multiple layers, higherstorage density is produced. In order to achieve a high throughout tocut costs, manufacturers typically enable high energy density and gasfeed to the reactor. However, as the thickness and number of layers forthe stacking film increases, poor energy distribution and uneven gasflow leads to a lack of uniformity in the stacked 3D NAND film.Accordingly, the lack of uniformity leads to a high in-plane distortion(IPD), which may result in the 3D NAND memory experiencing poorperformance issues.

IPD may be described by the overlay vector map on the wafer substrate.For locations on the wafer and/or for each logical unit (i.e., die), IPDmay include a vector having x-axis component and a y-axis component. Thevector may indicate a magnitude and direction of distortion at alocation on the wafer. Distortion may occur due to the incorrect energydistribution, gas flow non-uniformity, hardware issues, design issues,or other problems that may occur during a manufacturing process. Thenon-uniformity of an overlay vector distribution may be characterized byits three-sigma value. Three-sigma is a statistical tool used tocalculate probability. A three-sigma value may be used as a standard onwhether the IPD distribution meets industry requirements (e.g.,following lithography steps to attain a good yield). However, thethree-sigma value gives limited data regarding which area(s) needs to beimproved and/or optimized to lower in-plane distortion. Furthermore, thethree-sigma value fails to gauge the effect on IPD uniformity fromprocess or hardware changes.

The devices, systems, and methods disclosed herein use metrology data togenerate assessment maps from IPD maps associated with wafer substrates.Specifically, the devices, systems, and methods disclosed herein mayanalyze and decompose different aspects related to an IPD map. In afirst example, the system of the present disclosure may generate aplanar map to determine a planar component of the IPD map. A planarcomponent may indicate distortion due to a potential asymmetry of themanufacturing process, either in configuration of the hardware of themanufacturing equipment, or the manufacturing process (e.g., interactionbetween plasma and gas flow distribution). The planar map may indicate amagnitude of the planar component.

To generate the planar map, the system may first receive a first vectormap associated with manufacturing parameters of a substrate. The firstvector map may include a first set of vectors each indicating adistortion of a particular location on the substrate. The first vectormap may be an IPD map. The system may then generate a second vector mapincluding a second set of vectors by rotating a position of each vectorin the first set of vector components. The second set of vectors mayeach indicate a change in direction of the magnitude of the distortionof the particular location on the substrate. The system may thengenerate a third vector map including a third set of vectors based onvectors in the second set of vectors and corresponding vectors in thefirst set of vectors. The third set of vectors may reflect reduced noisein distortions across the locations on the substrate. The system maythen generate a fourth vector map by subtracting each vector of thethird set of vectors from a corresponding vector in the first set ofvectors. The fourth vector map may indicate a planar component of thefirst vector map.

In another example, the system may generate a radial map to determine aradial component of the IPD map. A radial component may indicatedistortion due to tensile and compressive stresses exerted across awafer. The direction and the three-sigma value of the radial map maygauge what effect the manufacturing process and hardware parts of themanufacturing equipment have on radial IPD of the substrate.

To generate the radial map, the system may receive a first vector mapassociated with manufacturing parameters of a substrate. The firstvector map may include a first set of vectors each indicating adistortion of a particular location of multiple locations on thesubstrate. The system may then generate a second vector map including asecond set of vectors by rotating a position of each vector in the firstset of vectors. The second set of vectors may each indicate a change indirection of the magnitude of the distortion of the particular locationon the substrate. The system may further generate a third vector mapincluding a third set of vectors based on vectors in the second set ofvectors and corresponding vectors in the first set of vectors. The thirdset of vectors may reflect reduced noise in distortions across theplurality of locations on the substrate. The system may further generatea fourth vector map including a fourth set of vectors by projecting adirection component of each vector in the third set of vectors to aradial direction. The system may further generate a fifth vector mapincluding a fifth set of vector components by grouping the vectors ofthe fourth set of vectors and determining a magnitude associated witheach group of vectors. The fifth vector map include the radial map, andmay indicate stresses and/or strains exhibited by the substrate.

In another example, the system may generate a residue map to determine aresidue component of the IPD map. A residue component may indicatelocalized defects caused by a hardware failure, a process instability, ahardware design flaw, etc. To generate the residue map, the system maygenerate a sixth vector map, including a sixth set of vectors, bysubtracting each vector in the fifth set of vectors from a correspondingvector in the first set of vectors.

Aspects of the present disclosure result in technological advantages ofsignificant reduction in energy consumption, product defects,performance issues, processor overhead, and so forth. In someembodiments, the technological advantages result from generatingassessment maps for each wafer produced by manufacturing equipment. Theassessment maps may include planar maps that detail planar components ofan IPD map, radial maps that detail radial components of the IPD map,and residue maps that detail residue components of the IPD map. Theassessment maps may allow a user or a system to determine manufacturinghardware and process issues that can lead to a defective or poorproduct. Furthermore, the assessment maps may allow a user or the systemto determine how to optimize or improve the manufacturing process, thusresulting in less energy consumption, less defective products, and animproved IPD when compared to conventional approaches.

FIG. 1 is a block diagram illustrating an exemplary system architecture100, according to certain embodiments. The system architecture 100includes a client device 106, an assessment map generating system 110, asensor system 120, a metrology system 130, and a data store 140. Theassessment map generating system 110 may include a planar map generator112 that generates planar maps 162, a radial map generator 114 thatgenerates radial maps 164, and a residue map generator 116 thatgenerates residue maps 166. The sensor system may 120 include a sensorserver 122 (e.g., field service server (FSS) at a manufacturingfacility), manufacturing equipment 124, sensors 126, and sensoridentifier reader 128 (e.g., front opening unified pod (FOUP) radiofrequency identification (RFID) reader for sensor system 120). Themetrology system 130 may include a metrology server 132 (e.g., metrologydatabase, metrology folders, etc.), metrology equipment 134, metrologyidentifier reader 136 (e.g., FOUP RFID reader for metrology system 130),and an in-plane distortion map generator 138.

The sensors 126 may provide sensor values 144 (e.g., manufacturingparameters) associated with producing corresponding product (e.g.,substrates or wafers) by manufacturing equipment 124. The sensor values144 may include a value of one or more of temperature (e.g., heatertemperature), spacing (SP), pressure, high frequency radio frequency(HFRF), voltage of electrostatic chuck (ESC), electrical current, flow,power, voltage, plasma and/or gas flow, energy distribution, etc. Sensorvalues 144 may be associated with or indicative of manufacturingparameters such as hardware parameters (e.g., settings or components(e.g., size, type, etc.) of the manufacturing equipment 124) or processparameters (e.g., flow rates) of the manufacturing equipment. The sensorvalues 144 may be provided while the manufacturing equipment 124 isperforming manufacturing processes (e.g., equipment readings whenprocessing wafers). The sensor values 144 may be different for eachproduct (e.g., each wafer).

The sensor identifier reader 128 (e.g., FOUP RFID reader for sensorsystem 120) may provide a sensor carrier identifier (e.g., FOUPidentifier, wafer carrier identifier, slot identifier, etc.). The sensorserver 122 may generate a sensor data identifier 146 that includes thesensor carrier identifier and a timestamp (e.g., date, time, etc.). Asensor carrier identifier may be a carrier identifier (e.g., FOUPidentifier, etc.) identified by the sensor system 120 (e.g., via sensoridentifier reader 128). The sensor server 122 may generate sensor data142 that includes sensor values 144 and a sensor data identifier 146. Insome embodiments, the sensor data 142 (e.g., sensor data identifiers146) further includes product identifiers 148. For example, multipleproducts (e.g., twenty-five wafers) may be associated with the samesensor carrier identifier and each product identifier 148 may indicatethe order of the products (e.g., first wafer, second wafer, etc. in thewafer carrier).

The metrology equipment 134 may provide metrology values 152 (e.g.,property data of wafers) associated with products (e.g., wafers)produced by the manufacturing equipment 124. The metrology values 152may include a value of one or more of film property data (e.g., waferspatial film properties), dimensions (e.g., thickness, height, etc.),in-plane distortions and/or uniformity, dielectric constant, dopantconcentration, density, defects, etc. The metrology values 152 may be ofa finished or semi-finished product. The metrology values 152 may bedifferent for each product (e.g., each wafer).

The metrology identifier reader 136 (e.g., FOUP RFID reader formetrology system 130) may provide a metrology carrier identifier (e.g.,FOUP identifier, wafer carrier identifier, slot identifier, etc.). Ametrology carrier identifier may be a carrier identifier (e.g., FOUPidentifier, etc.) identified by the metrology system 130 (e.g., viametrology identifier reader 136). The metrology carrier identifier andthe sensor carrier identifier that correspond to the same products(e.g., same wafers) may be the same carrier identifier (e.g., same FOUPID) and correspond to the same carrier (e.g., the same FOUP). Themetrology server 132 may generate metrology data identifiers 154 thatinclude the metrology carrier identifier and a timestamp (e.g., datestamp, etc.). The metrology server 132 may generate metrology data 150that includes metrology values 152 and a metrology data identifier 154.In some embodiments, the metrology data 150 further includes productidentifiers 156. For example, multiple products (e.g., twenty-fivewafers) may be associated with the same metrology data identifier 154(e.g., wafer carrier identifier) and each product identifier 156 mayindicate the order of the products (e.g., first wafer, second wafer,etc. in the wafer carrier).

In some embodiments, a product carrier (e.g., FOUP, wafer carrier) maytransfer the products from the manufacturing equipment 124 to themetrology equipment 134. The products may maintain the same order (e.g.,same location in the FOUP or wafer carrier) in the sensor system 120 andin the metrology system 130. For example, wafers may be loaded into andout of the manufacturing equipment 124 (e.g., for processing of thewafers and providing sensor data 142 via sensor server 122) in the sameorder as they are loaded into and out of metrology equipment 134 (e.g.,for providing metrology data 150 via metrology system 130). In someembodiments, the sensor carrier identifier (e.g., FOUP ID associatedwith sensor system 120) and the metrology carrier identifier (e.g., FOUPID associated with metrology system 130) that correspond to the sameproducts are associated with the same product carrier (e.g., the sameFOUP) and/or carrier identifier (e.g., the sensor carrier identifier andthe metrology carrier identifier are the same).

The IPD map generator 138 may generate an IPD map 158 from the metrologyvalues 152. The IPD map 158 may be an overlay vector map that includesdistortion vectors at each of multiple locations on a wafer, andcoordinates of die on the wafer. Each vector on the vector map mayinclude an x-axis component and a y-axis component. Each wafer producedby the manufacturing equipment 124 may have an IPD map 158 generatedbased on its metrology values 152. Each vector may be associated with alocation on the wafer, or with a die (e.g., logical unit) on the wafer.FIG. 5A is a graph showing an example IPD map. Specifically, FIG. 5Ashows an IPD map with vectors at multiple location, measured innanometers. Each IPD map 158 may include a three-sigma value for thex-axis components and the y-axis components.

Three-sigma is a statistical tool used to calculate probability. The IPDmap generator 138 can determine a three-sigma value for the x-axiscomponents by first calculating the standard deviation of the x-axiscomponents and for the y-axis components by first calculating standarddeviation of the y-axis components on an IPD map. Each standarddeviation may be multiplied by 3, and the product of the multiplicationmay be subtracted from a mean value (a mean value of x-axis componentsand a mean value of y-axis components). The resultant three-sigma valueindicates a high (e.g., 99.73%) chance that other vectors will have avalue (e.g., a magnitude) lower than the three-sigma value. As shown inFIG. 5A, the three-sigma value for the x-axis components is 12.3nanometers and the three-sigma value for the y-axis components is 11.9nanometers. Three-sigma values may be determined for every type ofvector map. For example, the planar map generator 112 may determinethree-sigma values for the planar maps 162, the radial map generator 114may determine three-sigma values for the radial maps 164, and theresidue map generator 116 may determine three-sigma values for theresidue maps 166. Additionally, three-sigma value may be determined, bythe assessment map generating system 110 for intermediate vector mapsgenerated during the process of generating the planar maps 162, theradial maps 164, and the residue maps 166. The will be explained ingreater detail below.

Returning to FIG. 1 , the client device 106, assessment map generatingsystem 110, sensor system 120 (e.g., sensor server 122, manufacturingequipment 124, sensors 126, sensor identifier reader 128, etc.),metrology system 130 (e.g., metrology server 132, metrology equipment134, metrology identifier reader 136, IPD map generator, etc.), and datastore 140 may be coupled to each other via a network 170 for generatingplanar maps 162, radial maps 164, and residue maps 166 to performcorrective actions by analysis component 108. The corrective action maybe based on data from correlation database 168. The correlation database168 may associate one or more types of deformations or flaws identifiedfrom one or more of the IPD map 158, the planar map 162, the radial map164, and/or the residue map to one or more causes of the deformationand/or flaw.

In some embodiments, network 170 is a public network that providesclient device 106 with access to the assessment map generating system110, data store 140, and other publically available computing devices.In some embodiments, network 170 is a private network that providesassessment map generating system 110 access to the sensor system 120,metrology system 130, data store 140, and other privately availablecomputing devices and that provides client device 106 access to the mapgenerating system 110, data store 140, and other privately availablecomputing devices. Network 170 may include one or more wide areanetworks (WANs), local area networks (LANs), wired networks (e.g.,Ethernet network), wireless networks (e.g., an 802.11 network or a Wi-Finetwork), cellular networks (e.g., a Long Term Evolution (LTE) network),routers, hubs, switches, server computers, cloud computing networks,and/or a combination thereof.

The client device 106 may include a computing device such as personalcomputers (PCs), laptops, mobile phones, smart phones, tablet computers,netbook computers, network connected televisions (“smart TV”),network-connected media players (e.g., Blu-ray player), a set-top-box,over-the-top (OTT) streaming devices, operator boxes, etc. The clientdevice 106 may be capable of obtaining metrology data (from data store140, from metrology system 130, etc.) associated with product (e.g.,substrates, wafers, die, etc.), produced by the manufacturing equipment124, receiving user input requesting one or more assessment maps 160 tobe generated by the assessment map generating system 110, receiving therequested assessment maps, obtaining sensor data associated with themanufacturing equipment 124 (e.g., from data store 140, from sensorsystem 120, etc.), and causing a corrective action (e.g., adjustment inthe manufacturing parameters of the manufacturing equipment 124) basedon the assessment maps. Each client device 106 may include an operatingsystem that allows users to one or more of generate, view, or edit data(e.g., indication associated with manufacturing equipment 124,corrective actions associated with manufacturing equipment 124, etc.).In some embodiments, the metrology data 150 corresponds to historicalproperty data of products (e.g., produced using manufacturing parametersassociated with sensor data 142).

Performing manufacturing processes the result in defective products canbe costly in time, energy, and manufacturing equipment 124 used to makethe defective products, the cost of identifying the defects anddiscarding the defective product, etc. By inputting current sensor dataand/or metrology data, receiving output of assessment maps 160, andperforming a corrective action based on the assessment maps 160, system100 can have the technical advantage of avoiding the cost of producing,identifying, and discarding defective products.

Manufacturing parameters may be suboptimal for producing products whichmay have costly results of increased resource (e.g., energy, coolant,gases, etc.) consumption, increased amount of time to produce theproducts, increased component failure, increased amounts of defectiveproducts, etc. By generating assessment maps 160 and analyzing theresults (e.g., planarity, deformations, stresses, strains,abnormalities, etc.) indicated in the assessment maps 160, and adjustingthe manufacturing parameters of the manufacturing equipment 124, system100 can have a technical advantage of using optimal manufacturingparameters (e.g., hardware parameters, process parameters, optimaldesign) to avoid costly results of suboptimal manufacturing parameters

Corrective action may be associated with one or more of computationalprocess control (CPC), statistical process control (SPC), automaticprocess control (APC), preventative operative maintenance, designoptimization, updating of manufacturing parameters, feedback control,machine learning modification, replacing or repairing a manufacturingcomponent, etc.

Sensor data 142 may be associated with manufacturing processes ofmanufacturing equipment 124 and metrology data 150 may be associatedwith properties of the finished product produced by the manufacturingprocesses. In another example, the manufacturing equipment may be a filmmaterial dispenser and the manufacturing process may be dispensing alayer of film onto a wafer. The sensor data 142 may indicate gas flowdistribution, flow rate, etc. The metrology data 150 may indicate a filmthickness, distortion, etc. The metrology data 150 may further indicatein-plane distortions on an IPD map (e.g., IPD maps 158).

The assessment map generating component 110 may use the IPD maps 158 togenerate assessment maps 160. The assessment maps 160 may assess an IPDmap and indicate deformation properties of the IPD map. For example, theassessment map generating component 110 may generate one or more planarmap 162, one or more radial maps 164, and one or more residue maps 166.The planar map may indicate a direction of a planarity of the wafer.Specifically, the planar map may indicate a direction of a slopingeffect produced by the stacked film on the wafer. The radial mapindicate stress and/or strains across the wafer (e.g., compressivestresses, tensile stresses, etc.). The residue map may indicateabnormalities on the wafer. Each of the assessment maps 160 may includea three-sigma value. The three-sigma value, the magnitude, and/or thedirection of the vectors on each of the assessment maps 160 may indicatethe severity of distortion, stress, abnormalities, etc. Based on theassessment maps 160, the client device 106 (e.g., via the analysiscomponent 108) may recommend corrective action or cause the correctiveaction to be performed, either from user input or automatically.

Manufacturing parameters may include hardware parameters (e.g.,replacing components, using certain components, etc.) and/or processparameters (e.g., temperature, pressure, flow, rate, etc.). In someembodiments, the corrective action is causing preventative operativemaintenance (e.g., replace, process, clean, etc. components of themanufacturing equipment 124). In some embodiments, the corrective actionis causing design optimization (e.g., updating manufacturing parameters,manufacturing processes, manufacturing equipment 124, etc. for anoptimized product). In an example, based on vector magnitudes, vectordirections, and/or the three-sigma value (when compared to a thresholdvalue) of the planar map, hardware configuration issues or anunfavorable interaction between plasma and gas flow distribution may beindicated. In another example, based on vector magnitudes, directions,and/or the three-sigma value of the radial map, energy distribution andgas flow issues can be indicated. In yet another example, based onvector magnitudes, directions, and/or the three-sigma values of theresidue map, hardware design flaws (e.g., an arching spot on anelectrode) can be indicated.

The client device 106 may include an analysis component 108. Analysiscomponent 108 may receive user input (e.g., via a GUI displayed via theclient device 106) of a request for assessment maps. In someembodiments, the analysis component 108 transmits the request to theassessment map generating system 110, receives output (e.g., assessmentmaps 160) from the assessment map generating system 110, and displaysthe assessment maps 160 for analysis. In some embodiments, the analysiscomponent 108 determines a corrective action based on the output. Insome embodiments, the analysis component 108 causes the correctiveaction to be implemented (e.g., changing a manufacturing parameter)automatically, or upon receiving user input.

Sensor server 122 and metrology server 132 may each include one or morecomputing devices such as a rackmount server, a router computer, aserver computer, a personal computer, a mainframe computer, a laptopcomputer, a tablet computer, a desktop computer, graphics processingunit (GPU), accelerator application-specific integrated circuit (ASIC)(e.g., tensor processing unit (TPU)), etc.

Data store 140 may be a memory (e.g., random access memory), a drive(e.g., a hard drive, a flash drive), a database system, or another typeof component or device capable of storing data. Data store 140 mayinclude multiple storage components (e.g., multiple drives or multipledatabases) that may span multiple computing devices (e.g., multipleserver computers). The data store 140 may store sensor data 142,metrology data 150, and assessment maps 160.

Sensor data 142 may include sensor values, sensor data identifiers 146,and product identifiers 148. Metrology data 150 may include metrologyvalues 152, metrology data identifiers 154, product identifiers 156, andIPD maps 158. Each instance (e.g., set) of sensor data 142 maycorrespond to a corresponding product carrier (e.g., associated with asensor data identifier 146), a corresponding timestamp (e.g., associatedwith the sensor data identifier 146), and/or a corresponding product(e.g., associated with a product identifier 148). Each instance (e.g.,set) of metrology data 150 may correspond to a corresponding productcarrier (e.g., associated with a metrology data identifier 154), acorresponding timestamp (e.g., associated with the metrology dataidentifier 154), and/or a corresponding product (e.g., associated with aproduct identifier 156).

In some embodiments, the functions of client device 106, assessment mapgenerating system 110, sensor server 122, metrology server 132, may beprovided by a fewer number of machines. In some embodiments, assessmentmap generating system 110, sensor server 122, and metrology server 132,may be integrated into a single machine.

It should be noted that functions described in one embodiment as beingperformed by client device 106, sensor server 122, metrology server 132,can also be performed on the assessment map generating system 110 inother embodiments, if appropriate. In addition, the functionalityattributed to a particular component can be performed by different ormultiple components operating together. The assessment map generatingsystem 110 may be accessed as a service provided to other systems ordevices through appropriate application programming interfaces (API).

In embodiments, a “user” may be represented as a single individual.However, other embodiments of the disclosure encompass a “user” being anentity controlled by a plurality of users and/or an automated source.For example, a set of individual users federated as a group ofadministrators may be considered a “user.”

Although embodiments of the disclosure are discussed in terms ofgenerating assessment maps 160 to perform a corrective action inmanufacturing facilities (e.g., semiconductor manufacturing facilities),embodiments may also be generally applied to aggregating types of datato perform an action. Embodiments may be generally applied tointegrating different types of data. For example, sensor data may beaggregated with corresponding component failure data for predicting endof life of components. In another example, images may be aggregated withcorresponding image classification for predicting image classificationof images.

FIG. 2 is a block diagram illustrating a system 200 for generatingassessment maps (e.g., assessment maps 160 of FIG. 1 ) using IPD maps258 (e.g., IPD maps 158 of FIG. 1 ), according to certain embodiments.The system of FIG. 2 shows data inputs 205, assessment map generatingsystem 210, and data outputs 220.

In some embodiments, the assessment map generating system 210 mayreceive one or more data inputs 205 (e.g., one or more IPD maps 258).The one or more data inputs 205 may be sent to the assessment mapgenerating system 210 automatically, or by user input (e.g., a request).In some embodiments, the planar map generator 212 (e.g., planar mapgenerator 112 of FIG. 1 ) may generate one or more planar maps 262. Theprocess of generating a planar map will be discussed in greater detailin FIG. 3 . In some embodiments, the radial map generator 214 (e.g.,radial map generator 114 of FIG. 1 ) may generate one or more radialmaps 264. The process of generating a radial map will be discussed ingreater detail in FIG. 4 . In some embodiments, the residue mapgenerator 216 (e.g., planar map generator 116 of FIG. 1 ) may generateone or more residue maps 266. The process of generating a residue mapwill be discussed in greater detail in FIG. 4 .

FIG. 3 is a flow diagram of a method 300 for generating a planar map(e.g., planar map 162 of FIG. 1 ), according to certain embodiments.Method 300 may be performed by processing logic that may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, processing device, etc.), software (such as instructions runon a processing device, a general purpose computer system, or adedicated machine), firmware, microcode, or a combination thereof. Inone embodiment, method 300 may be performed, in part, by assessment mapgenerating system 110 (e.g., planar map generator 112). In someembodiments, a non-transitory storage medium stores instructions thatwhen executed by a processing device (e.g., of assessment map generatingsystem 110) cause the processing device to perform method 300.

For simplicity of explanation, method 300 is depicted and described as aseries of acts. However, acts in accordance with this disclosure canoccur in various orders and/or concurrently and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be performed to implement the method 300 in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the method 300 could alternatively berepresented as a series of interrelated states via a state diagram orevents.

Due to a potential asymmetry of the manufacturing process, either inconfiguration of the hardware of the manufacturing equipment 124, or themanufacturing process (e.g., interaction between plasma and gas flowdistribution), an IPD map may have a planar component, which can cause ahigh in-plane distortion. The operations of method 300 generate a planarmap, which indicates a magnitude of the planar component.

Referring to FIG. 3 , at block 302, the processing logic receives afirst vector map associated with manufacturing parameters of a substrate(e.g., wafer). The first vector map may be an IPD map (e.g., IPD map158) and will be referred to as an IPD map, hereafter. The IPD map mayinclude a first set of vectors where each vector indicates a distortionof a particular location on the wafer. Each vector of the IPD map mayhave an x-axis component and a y-axis component indicating direction andmagnitude. The IPD map may be created using metrology data from themetrology system 130, and stored in a data store (e.g., data store 140of FIG. 1 ), and the processing logic may obtain (e.g., retrieve) theIPD may from the data store. In an example, the IPD map may becalculated based on the changes of surface slopes of the substrate.

At block 304, the processing logic generates a second vector mapincluding a second set of vectors. The processing logic may generate thesecond vector map by rotating a position of each vector in the first setof vectors. In an example, each vector in the second set of vectors isrotated by approximately or exactly 180 degrees. The second set ofvectors may each indicate a change in direction of the magnitude of adistortion at a particular location on the substrate. The second vectormap may be stored in a cache or memory component of the assessment mapgenerating system 110, or in the data store 140. The second vector mapmay be stored temporarily while method 300 is performed, or permanently.

At block 306, the processing logic generates a third vector mapincluding a third set of vectors. The third set of vectors may be basedon vectors in the second set of vectors and corresponding vectors in thefirst set of vectors. In an example, the processing logic generates thethird vector map by adding the vectors on the first set of vectors totheir respective locations on the second vector map. The sum at eachlocation may then be divided by 2 to generate the third set of vectors.The third set of vectors may reflect reduced noise in distortions acrossthe locations on the substrate. The processing logic may generate athree-sigma value for the x-axis component and for the y-axis componentof the third vector map. FIG. 5B is a graph showing an example of athird vector map, which includes a third set of vectors. The thirdvector map shown in FIG. 5B is generated by applying the above steps ofmethod 300 to the IPD map shown in FIG. 5A. The three-sigma value forthe x-axis of the third vector map in FIG. 5B is 5.6 nm and thethree-sigma value for the y-axis component is 5.1 nm.

Returning to FIG. 3 , at block 308, the processing logic generates afourth vector map including a fourth set of vectors. The fourth vectormap may be a planar map (e.g., planar map 162) and will be referred toas a planar map, hereafter. In an example, the processing logicgenerates the planar map by subtracting each vector of the third set ofvectors from a corresponding vector component in the first set ofvectors. The planar map indicates a planar component of the IPD map.Based on the directions and the magnitudes of the fourth set of vectors,the processing logic may determine a direction of the planar component.The processing logic may generate a three-sigma value for the x-axiscomponent and for the y-axis component of the planar map. FIG. 5C is agraph showing an example of the planar map. The three-sigma value forthe x-axis of the graph in FIG. 5C is 10.7 nm and the three-sigma valuefor the y-axis component is 10.6 nm. The planar direction is indicatedby arrow 530. The planar map allows for quantification (e.g., viamagnitude and direction) of the effect on planar IPD from hardwarecomponents and manufacturing processes. The analysis component 108 mayuse the correlation database to associate one or more deformed or flawedsections of the planar map with one or more causes (e.g., aconfiguration of the hardware of the manufacturing equipment, amanufacturing process parameter, such as an interaction between plasmaand gas flow distribution, etc.). The analysis component 108 maygenerate a recommendation based on the planar map. In an example, theanalysis component 108 may recommend a design optimization, amanufacturing part replacement, and/or a manufacturing processadjustment to minimize the planar component. In another example, theanalysis component 108 may automatically perform a corrective actionbased on the planar map, such as, for example, adjust the gas flowdistribution, perform pedestal heater leveling to correct planarcontribution, etc.

FIG. 4 is a flow diagram of a method 400 for generating a radial map(e.g., radial map 164 of FIG. 1 ) and a residue map (e.g., residue map166 of FIG. 1 ), according to certain embodiments. Method 400 may beperformed by processing logic that may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, processingdevice, etc.), software (such as instructions run on a processingdevice, a general purpose computer system, or a dedicated machine),firmware, microcode, or a combination thereof. In one embodiment, method400 may be performed, in part, by assessment map generating system 110(e.g., radial map generator 114 and/or residue map generator 116). Insome embodiments, a non-transitory storage medium stores instructionsthat when executed by a processing device (e.g., of vector mapgenerating system 110) cause the processing device to perform method400.

For simplicity of explanation, method 400 is depicted and described as aseries of acts. However, acts in accordance with this disclosure canoccur in various orders and/or concurrently and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be performed to implement the method 400 in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the method 400 could alternatively berepresented as a series of interrelated states via a state diagram orevents. Portions of method 400 may be the similar or the same as blocksof method 300 of FIG. 3 .

Referring to FIG. 4 , at block 402, the processing logic receives afirst vector map associated with manufacturing parameters of a substrate(e.g., wafer). The first vector map may be an IPD map (e.g., IPD map158) and will be referred to as an IPD map, hereafter. The IPD map mayinclude a first set of vectors where each vector indicates a distortionof a particular location on the substrate. Each vector may have anx-axis component and a y-axis component. The IPD map may be obtained(e.g., retrieved) from a data store (e.g., data store 140 of FIG. 1 ).

At block 404, the processing logic generates a second vector mapincluding a second set of vectors. The processing logic may generate thesecond vector map by rotating a position of each vector in the first setof vectors. In an example, each vector in the second set of vectors isrotated by approximately or exactly 180 degrees. The second set ofvectors may each indicate a change in direction of the magnitude of thedistortion of the particular location on the substrate. The secondvector map may be stored in a cache or memory component of the vectormap generating system 110, or in the data store 140. The second vectormap may be stored temporarily for use during method 400, or permanently.

At block 406, the processing logic generates a third vector mapincluding a third set of vectors. The third set of vectors may be basedon vectors in the second set of vectors and corresponding vectors in thefirst set of vectors. In an example, the processing logic generates thethird vector map by adding the vectors on the first set of vectors totheir respective locations on the second vector map. The sum at eachlocation may then be divided by 2 to generate the third set of vectors.The third set of vectors may reflect reduced noise in distortions acrossthe locations on the substrate.

At block 408, the processing logic generates a fourth vector mapincluding a fourth set of vectors. In an example, the processing logicgenerates the fourth vector map by projecting a direction component ofeach vector in the third set of vectors to a radial direction.

At block 410, the processing logic generates a fifth vector mapincluding a fifth set of vectors. The fifth vector map may be a radialmap (e.g., radial map 164) and will be referred to as a radial map,hereafter. In an example, the processing logic may group the vectors ofthe fourth set of vectors and determine an average magnitude associatedwith each group (or radius) of vectors. The direction of the vectors mayremain unchanged. The radial map may indicate stresses and/or strainsexhibited across the substrate. In particular, on each group in theradial map, all the vectors may have the same magnitude (e.g., a radialvector), and be directed either towards the center or away from thecenter. A direction towards the center man indicate that stress and/orstrains in that group are compressive, which may indicate that energydensity is high. A direction away from the center may indicate tensilelocal stress and/or strains, which may indicate that the energy densityis relatively lower. In an example, since each radial vector may bebi-directional, the processing logic may assign positive and negativesigns to each radial vector based on the direction of the radial vector.For example, a positive sign may be assigned to radial vectors having adirection towards the center (which indicates compressive stress), and anegative sign may be assigned to radial vectors having a direction awayfrom the center (which indicates tensile stress). In another example,each radial vector may be assigned a color based on its direction andmagnitude. In yet another example, any combination of signs and colorscan be used to indicate stresses and/or magnitudes on the fifth vectormap. The processing logic may generate a three-sigma value for thex-axis component and for the y-axis component of the radial map.

FIG. 5D is a graph showing an example radial map. The three-sigma valuefor the x-axis of the graph in FIG. 5D is 5.3 nm and the three-sigmavalue for the y-axis component is 5.3 nm. As seen, the radial mapdisplays multiples groups including a radial vector. Different shadesare associated with each group (or radius), and the shade indicateswhether the group is compressive (and at what magnitude) or whether thegroup is tensile (and at what magnitude).

The direction and the three-sigma value for the radial map may allow auser or the analysis component 108 to gauge the what effect themanufacturing process and hardware parts of the manufacturing equipmenthave on radial IPD of the substrate. The analysis component 108 maygenerate a recommendation based on the radial map. In an example, theanalysis component 108 may recommend a design optimization, amanufacturing part replacement, and/or a manufacturing processadjustment to minimize the radial component (e.g., minimize stressesand/or strains experienced by the substrate). In another example, theanalysis component 108 may automatically perform a corrective actionbased on the planar map.

Returning to FIG. 4 , at block 412, the processing logic generates asixth vector map including a sixth set of vectors. The sixth vector mapmay be a residue map (e.g., residue map 164) and will be referred to asa residue map, hereafter. The processing logic may generate the residuemap by subtracting each vector in the fifth set of vectors from acorresponding vector in the first set of vectors. The processing logicmay generate a three-sigma value for the x-axis component and for they-axis component of the radial map. The sixth set of vectors on theresidue map may be scattered in direction and magnitude. A highmagnitude in a location in the residue map may indicate a hardwarefailure, a process non-stability, or any other hardware or manufacturingprocesses that cause localized defects. For example, a pattern mayemerge in a particular location, which may indicate a hardware designflaw. In response, the analysis component 108 may issue a recommendationto change a hardware component, or recommend generating a new design toimprove process stability.

FIG. 5E is a graph showing an example residue map. The three-sigma valuefor the x-axis of the residue map in FIG. 5D is 0.7 nm and thethree-sigma value for the y-axis component is 1.2 nm. As seen, theresidue map displays small magnitude values and small sigmathree-values, which may indicate that the no local abnormalities in theIPD map.

The analysis component 108 may generate a recommendation, using thecorrelation database 168, based on the residue map. In an example, theanalysis component 108 may recommend a design optimization, amanufacturing part replacement, and/or a manufacturing processadjustment to minimize or eliminate residue component. In anotherexample, the analysis component 108 may automatically perform acorrective action based on the residue map.

FIG. 6 is a block diagram illustrating a computer system 600, accordingto certain embodiments. In some embodiments, computer system 600 may beconnected (e.g., via a network, such as a Local Area Network (LAN), anintranet, an extranet, or the Internet) to other computer systems.Computer system 600 may operate in the capacity of a server or a clientcomputer in a client-server environment, or as a peer computer in apeer-to-peer or distributed network environment. Computer system 600 maybe provided by a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, switch or bridge, or any devicecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that device. Further, the term“computer” shall include any collection of computers that individuallyor jointly execute a set (or multiple sets) of instructions to performany one or more of the methods described herein.

In a further aspect, the computer system 600 may include a processingdevice 602, a volatile memory 604 (e.g., random access memory (RAM)), anon-volatile memory 606 (e.g., read-only memory (ROM) orelectrically-erasable programmable ROM (EEPROM)), and a data storagedevice 616, which may communicate with each other via a bus 608.

Processing device 602 may be provided by one or more processors such asa general purpose processor (such as, for example, a complex instructionset computing (CISC) microprocessor, a reduced instruction set computing(RISC) microprocessor, a very long instruction word (VLIW)microprocessor, a microprocessor implementing other types of instructionsets, or a microprocessor implementing a combination of types ofinstruction sets) or a specialized processor (such as, for example, anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), or a networkprocessor).

Computer system 600 may further include a network interface device 622.Computer system 600 also may include a video display unit 610 (e.g., anLCD), an alphanumeric input device 612 (e.g., a keyboard), a cursorcontrol device 614 (e.g., a mouse), and a signal generation device 620.

In some implementations, data storage device 616 may include anon-transitory computer-readable storage medium 624 on which may storeinstructions 626 encoding any one or more of the methods or functionsdescribed herein, including instructions encoding components of FIG. 1(e.g., assessment map generating system 110, analysis component 108,etc.) and for implementing methods described herein.

Instructions 626 may also reside, completely or partially, withinvolatile memory 604 and/or within processing device 602 during executionthereof by computer system 600, hence, volatile memory 604 andprocessing device 602 may also constitute machine-readable storagemedia.

While computer-readable storage medium 624 is shown in the illustrativeexamples as a single medium, the term “computer-readable storage medium”shall include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of executable instructions. The term“computer-readable storage medium” shall also include any tangiblemedium that is capable of storing or encoding a set of instructions forexecution by a computer that cause the computer to perform any one ormore of the methods described herein. The term “computer-readablestorage medium” shall include, but not be limited to, solid-statememories, optical media, and magnetic media.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and computer programcomponents, or in computer programs.

Unless specifically stated otherwise, terms such as “receiving,”“determining,” “generating,” “storing,” “causing,” “training,”“interrupting,” “selecting,” “providing,” “displaying,” or the like,refer to actions and processes performed or implemented by computersystems that manipulates and transforms data represented as physical(electronic) quantities within the computer system registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices. Also, the terms“first,” “second,” “third,” “fourth,” etc. as used herein are meant aslabels to distinguish among different elements and may not have anordinal meaning according to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor performing the methods described herein, or it may include a generalpurpose computer system selectively programmed by a computer programstored in the computer system. Such a computer program may be stored ina computer-readable tangible storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform methods described herein and/or each oftheir individual functions, routines, subroutines, or operations.Examples of the structure for a variety of these systems are set forthin the description above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples and implementations, itwill be recognized that the present disclosure is not limited to theexamples and implementations described. The scope of the disclosureshould be determined with reference to the following claims, along withthe full scope of equivalents to which the claims are entitled.

What is claimed is:
 1. A system comprising: a memory; and a processingdevice, coupled to the memory, to: receive a first vector map associatedwith manufacturing parameters of a substrate, wherein the first vectormap comprises a first set of vectors each indicating a distortion of aparticular location of a plurality of locations on the substrate;generate a second vector map comprising a second set of vectors byrotating a position of each vector in the first set of vectors, thesecond set of vectors each indicating a change in direction of amagnitude of the distortion of the particular location on the substrate;generate a third vector map comprising a third set of vectors based on acomputation of vectors in the second set of vectors and correspondingvectors in the first set of vectors, the third set of vectors reflectingreduced noise in distortions across the plurality of locations on thesubstrate; and generate a fourth vector map by subtracting each vectorof the third set of vectors from a corresponding vector in the first setof vectors, wherein the fourth vector map indicates a planar componentof the first vector map, indicative of a direction of a sloping effectproduced by stacked film on the substrate, of the first vector map. 2.The system of claim 1, wherein the first vector map comprises anin-plane distortion map.
 3. The system of claim 1, wherein theprocessing device is further to: generate a three-sigma value for anx-axis component of the fourth vector map; and generate a three-sigmavalue for a y-axis component of the fourth vector map.
 4. The system ofclaim 1, wherein the processing device is further to: recommend acorrective action based on the fourth vector map.
 5. The system of claim1, wherein the processing device is further to: automatically perform acorrective action based on the fourth vector map.
 6. A methodcomprising: receiving a first vector map associated with manufacturingparameters of a substrate, wherein the first vector map comprises afirst set of vectors each indicating a distortion of a particularlocation of a plurality of locations on the substrate; generating asecond vector map comprising a second set of vectors by rotating aposition of each vector in the first set of vectors, the second set ofvectors each indicating a change in direction of a magnitude of thedistortion of the particular location on the substrate; generating athird vector map comprising a third set of vectors based on acomputation of vectors in the second set of vectors and correspondingvectors in the first set of vectors, the third set of vectors reflectingreduced noise in distortions across the plurality of locations on thesubstrate; and generating a fourth vector map by subtracting each vectorof the third set of vectors from a corresponding vector in the first setof vectors, wherein the fourth vector map indicates a planar component,indicative of a direction of a sloping effect produced by stacked filmon the substrate, of the first vector map.
 7. The method of claim 6,wherein the first vector map comprises an in-plane distortion map. 8.The method of claim 6, further comprising: generating a three-sigmavalue for an x-axis component of the fourth vector map; and generating athree-sigma value for a y-axis component of the fourth vector map. 9.The method of claim 6, further comprising: recommending a correctiveaction based on the fourth vector map.
 10. The method of claim 6,further comprising: automatically performing a corrective action basedon the fourth vector map.
 11. A non-transitory computer-readable storagemedium comprising instructions that, when executed by a processingdevice operatively coupled to a memory, performs operations comprising:receiving a first vector map associated with manufacturing parameters ofa substrate, wherein the first vector map comprises a first set ofvectors each indicating a distortion of a particular location of aplurality of locations on the substrate; generating a second vector mapcomprising a second set of vectors by rotating a position of each vectorin the first set of vectors, the second set of vectors each indicating achange in direction of a magnitude of the distortion of the particularlocation on the substrate; generating a third vector map comprising athird set of vectors based on a computation of vectors in the second setof vectors and corresponding vectors in the first set of vectors, thethird set of vectors reflecting reduced noise in distortions across theplurality of locations on the substrate; and generating a fourth vectormap by subtracting each vector of the third set of vectors from acorresponding vector in the first set of vectors, wherein the fourthvector map indicates a planar component of the first vector map,indicative of a direction of a sloping effect produced by stacked filmon the substrate, of the first vector map.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein the first vectormap comprises an in-plane distortion map.
 13. The non-transitorycomputer-readable storage medium of claim 11, wherein the operationsfurther comprise: generating a three-sigma value for an x-axis componentof the fourth vector map; and generating a three-sigma value for ay-axis component of the fourth vector map.
 14. The non-transitorycomputer-readable storage medium of claim 11, wherein the operationsfurther comprise: recommending a corrective action based on the fourthvector map.
 15. The non-transitory computer-readable storage medium ofclaim 11, wherein the operations further comprise: automaticallyperforming a corrective action based on the fourth vector map.